Adaptive license plate image extraction
CompSysTech '04 Proceedings of the 5th international conference on Computer systems and technologies
Robust Car License Plate Localization Using a Novel Texture Descriptor
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Car plate localization using pulse coupled neural network in complicated environment
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
An efficient method based on orientation field for detection of license plates
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Learning-based license plate detection in vehicle image database
International Journal of Intelligent Information and Database Systems
Car plate localization using modified PCNN in complicated environment
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Real-Time license plate detection under various conditions
UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
Automatic license plate recognition system based on color image processing
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
An algorithm for accuracy enhancement of license plate recognition
Journal of Computer and System Sciences
A vehicle license plate detection method using region and edge based methods
Computers and Electrical Engineering
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A robust approach for extracting car license plate from images with complex background and relatively poor quality is presented in this paper. The approach focuses on dealing with images taken under weak lighting condition. The proposed method is divided into two steps: 1) searching candidate areas from the input image using gradient information, and 2) determining the plate area among the candidates and adjusting the boundary of the area by introducinga plate template. A set of experiments has been performed to prove the robustness and accuracy of the approach. For many images collected from a large underground parkingplace the result shows that 90% of them are correctly segmented.